AI Workflow Lead

White Label Storage is a venture-backed operator that leverages technology and AI to manage self-storage facilities across the United States. The AI Workflow Lead will drive automation solutions by identifying manual workflows and collaborating with the engineering team to enhance operational efficiency through agentic systems.

Responsibilities:

• Identify high-leverage opportunities by embedding with ops teams to deeply learn their manual workflows
• Scope automation solutions and partner with engineering to ship production-ready agentic systems that replace manual site manager tasks with resilient, auditable equivalents
• Define the logic and triggers for automated, context-aware communications to tenants, operators, and internal teams — and work with engineering to implement them
• Develop reusable prompt libraries, agent templates, and internal toolkits that can be scaled and rolled out to non-technical end-users across the organization
• Own the Automation Roadmap: Evaluate operational requests against business ROI and technical feasibility to ensure the team builds only high-leverage solutions
• Exercise the judgment to decline low-impact or fragile automations in favor of scalable, core systemic improvements
• Define success metrics (e.g., hours saved, revenue recovered) to move beyond 'functional' code to 'valuable' business outcomes
• Navigate the tension between urgent operational fixes and long-term AI infrastructure
• Audit and restructure the existing knowledge base to be LLM-ready: clear, structured, and optimized for RAG and agent reasoning
• Build sustainable processes for maintaining knowledge accuracy as operations evolve, policies change, and new facilities onboard
• Collaborate with ops leads to extract unwritten institutional expertise and formalize it into documented, reusable formats
• Establish quality standards for knowledge entries to reduce hallucination risk and ensure agents can act with high confidence
• Define and own observability requirements to track agent activity, error rates, and human escalations in production
• Establish feedback loops so agents and prompts improve over time based on real operational outcomes and failure modes
• Proactively flag risks and edge cases to leadership before they surface in production
• Own the entire process from writing scope documents and securing stakeholder buy-in to final shipping
• Work with the engineering team to ensure automations are deployed securely against production systems (RDS, DynamoDB, Databricks, and Sigma)